Recently, to address the limitations of these traditional sensor-based methods, vision-based methods for FoG detection [1]}, [2]} have been proposed by taking advantage of its natural and unobtrusive characteristics [3]}. These studies collect videos from clinical assessments during which the subject follows instructions to participate in a timed up and go (TUG) gait test. Such TUG tasks assess the functional mobility of a person in a standardized fashion [4]}.
Deep learning architectures are devised to characterize and detect fine-grained FoG patterns in these studies due to their great success in solving many vision related tasks.
Although these studies have achieved promising results, there may be potential issues when applying them in practical environments which are generally more complex compared with the experimental protocols used during clinical data collection.
For example, these studies work for cameras with fixed placements and require well-lit conditions, without shadows and occlusions.
